Part 2: Why just looking at info on “Chinese” and “income” in a few areas is no way to understand anything

We all jump to conclusions quickly. Journalists are the most famous for it — three of anything within a week is a trend. But, in fact, we all do it, journalist or not, in an attempt to understand the world’s complexity.

So it’s normal to look at a few census tracts and think, Gee, that’s an area where I know a lot of new Chinese immigrants live (or that’s what the neighbours say). And it’s an expensive area. And it looks like a lot of people are claiming they have low incomes there. So, it must be that …

The flaw is that we don’t look beyond that. We don’t look at the neighbourhood next door, one that also has a lot of new Chinese immigrants, one that’s also in an expensive area, but one where the rate of low-income households is not that high. Or the area that is expensive and that has a high rate of low-income households but where the level of new Chinese immigrants is low.

Academic researchers run what are called regression analyses to test whether there is really a connection between different variables.

It’s a standard feature of almost any study that attempts to prove anything. Do people who do strength training have better results in retaining memory functions than people who do yoga? You need to make sure that the strength-trainers and the yoga-ites are comparable on every other scale: a mix of ages and occupations, a similar range of diets, comparable levels of crossword-puzzle and other brain-boosting activities, and so on. Otherwise you run the risk of concluding that strength training is better, when actually the kind of people who like to do strength training also do a number of other related things that tend to boost memory.

My last post got kind of long, so I skipped talking about this.

But I’m adding this to make the point to everyone trying to understand Vancouver by looking at a few census tracts at a time. It can’t be done, or at least not to the level that any serious researcher would think was credible. You have to look at the region overall and figure out, i.e. does the number of new Chinese immigrants correlate at a significantly high rate with the level of low-income households in that tract. (It’s also good if you can do these kinds of correlation over time as well i.e. as more and more new Chinese immigrants move into an area, does the rate of this or that other factor increase as well in something approaching lockstep?)

This is true for some of the research showing up related to students or homemakers being listed on land titles as owners. On the face of it, it seems weird that people in those categories are listed as owners in expensive areas. But to make the case absolutely solidly, you’d need to look at what occupations are listed everywhere. Maybe it will show that a suspiciously high number of homemakers and students are buying only in particular high-end areas. But maybe it will show that a lot of homes throughout the region are allegedly owned by homemakers and students and that there’s barely a difference between the west side and anywhere else in town.

Yes, it’s a lot of work to do that. Hardly anyone is doing that kind of work. Well, except for one of my researcher friends who has provided the analysis below. He ran the variables through the program to see whether there were any distinct correlations. There weren’t, except for very minor effects. I’ve provided his full analysis below. I can’t understand more than a quarter of it, but maybe some of you can. I can understand enough to see that, yet again, there’s no smoking gun yet.

I hope everyone gets that I’m doing this because I believe that ideas should be tested. It’s dangerous to have everyone spouting the same conventional wisdom.

These regressions test for correlates across 450 Vancouver CMA census tracts. Est. percentage in tract with shelter costs greater than income (not sure how 0 income and 0 costs are treated). Like all regressions the results assume that the other variable values re not changing so higher X keeping all other variables unchanged (which is hard in reality if you think of increasing the number of recent immigrants while keeping the total number of immigrants unchanged.

This percentage in a tract is higher in tracts with more density, higher median house values, higher median rents, and lower median income. It falls with the total number of immigrants, but rises with the number of new (past 5 years) immigrants. Higher for Chinese recent immigrants, lower for Indian, and Filipino.

Mean # of Chinese recent immigrants is 80, Standard deviation is 135. Increasing the number by 135 (a 168% increase) would raise the HH of people in a census tract reporting shelter costs > income from 7.22 to 8.30 (an increase of 18%). Or another way, to increase the number of households reporting shelter costs > income by one, you would need to add 106 more Chinese recent immigrants to the tract. Half of this effect is common to all recent immigrants, the marginal effect if they are Chinese is thus just half of this

In the regression below, I add a dummy variable (=1 if the tract has a median value > 1.25M and then interact this with the # of recent Chinese immigrants. The idea is to try to see if it is high house price / high Chinese immigrants tracts that have uniquely higher rates. Main point, is that no correlation between higher # of recent Chinese immigrants in higher house price tracts and HH reporting shelter costs > income.

. * REGRESSIONS ON PERCENTAGE OF OWNER HH’S PAYING > 30% OF INCOME ON SHELTER

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Here I look at owner HH paying more than 30% of income. What is striking is that it is not correlated with median tract house value. Falls with median tract income, higher in tracts w/ more recent immigrants, but not especially Chinese recent immigrants, though it I correlated with more recent immigrants from India. That the recent Chinese immigrant correlation is not present for this regression (mainly because of a less precisely estimated effect, so there is more noise in the connection)

. * REGRESSIONS ON PERCENTAGE OF RENTER HH’S PAYING > 30% OF INCOME ON SHELTER

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Finally, the same exercise for the percentage of renter HH paying more than 30% of their income on shelter. As expected, higher percentage of renter HH w/ shter costs > 30% of income in tracts with higher median values and rents and lower median incomes. Higher where there are more recent immigrants, but lower in tracts with more recent immigrant from India